Smoking Cessation System for Preemptive Smoking Detection.
Smoking Cessation System for Preemptive Smoking Detection.
复制标题
DOI：
10.1109/jiot.2021.3097728
复制
发表时间：
2022-03
影响因子：
10.6
通讯作者：
Huang, Ming-Chun
中科院分区：
文献类型：
--
作者：
Maguire, Gabriel;Chen, Huan;Schnall, Rebecca;Xu, Wenyao;Huang, Ming-Chun
关键词：
戒烟对于许多对香烟和烟草上瘾的人来说是一个重大挑战。戒烟的移动的健康相关研究主要集中在使用自我报告或传感器监测技术的移动的电话数据收集上。在过去的5年里，随着智能手表设备的日益普及，人们进行了研究，根据从用户智能手表内部传感器分析的加速度计数据来预测与吸烟行为相关的吸烟动作。以前的吸烟检测方法集中于对当前用户吸烟行为进行分类。对于许多试图戒烟的用户来说，这种形式的检测可能是不够的，因为用户已经复吸。在本文中，我们提出了一种利用智能手表和手指传感器的戒烟系统，该系统能够检测吸烟前的活动，以阻止用户未来的吸烟行为。吸烟前的活动包括抓起一包香烟或点燃一支香烟，这些活动通常会立即被吸烟所取代。因此，通过准确检测吸烟前的活动，我们可以在使用者复吸之前提醒他们。我们的戒烟系统结合了来自智能手表的数据，用于提供总的加速度计和陀螺仪信息，以及可穿戴手指传感器的详细手指弯曲角度信息。我们比较了仅智能手表系统与智能手表和手指传感器组合系统的结果，以说明每个系统的准确性。结合智能手表和手指传感器系统的吸烟前活动分类准确率为80.6%，而仅智能手表系统的准确率为47.0%。
Smoking cessation is a significant challenge for many people addicted to cigarettes and tobacco. Mobile health-related research into smoking cessation is primarily focused on mobile phone data collection either using self-reporting or sensor monitoring techniques. In the past 5 years with the increased popularity of smartwatch devices, research has been conducted to predict smoking movements associated with smoking behaviors based on accelerometer data analyzed from the internal sensors in a user’s smartwatch. Previous smoking detection methods focused on classifying current user smoking behavior. For many users who are trying to quit smoking, this form of detection may be insufficient as the user has already relapsed. In this paper, we present a smoking cessation system utilizing a smartwatch and finger sensor that is capable of detecting pre-smoking activities to discourage users from future smoking behavior. Pre-smoking activities include grabbing a pack of cigarettes or lighting a cigarette and these activities are often immediately succeeded by smoking. Therefore, through accurate detection of pre-smoking activities, we can alert the user before they have relapsed. Our smoking cessation system combines data from a smartwatch for gross accelerometer and gyroscope information and a wearable finger sensor for detailed finger bend-angle information. We compare the results of a smartwatch-only system with a combined smartwatch and finger sensor system to illustrate the accuracy of each system. The combined smartwatch and finger sensor system performed at an 80.6% accuracy for the classification of pre-smoking activities compared to 47.0% accuracy of the smartwatch-only system.
登录
查看更多内容
影响因子：
3
作者：
Gentner, Reinhard;Classen, Joseph
通讯作者：
Classen, Joseph
影响因子：
6.2
作者：
Glauser, Oliver;Wu, Shihao;Sorkine-Hornung, Olga
通讯作者：
Sorkine-Hornung, Olga
影响因子：
2.4
作者：
Rezzoug, Nasser;Gorce, Philippe
通讯作者：
Gorce, Philippe
影响因子：
3.6
作者：
Chauhan, Raghuraj;Sebastian, Bijo;Ben-Tzvi, Pinhas
通讯作者：
Ben-Tzvi, Pinhas
DOI：
10.1016/j.ergon.2010.11.001
发表时间：
2011-01-01
影响因子：
3.1
作者：
Bae, Sungchan;Armstrong, Thomas J.
通讯作者：
Armstrong, Thomas J.